shap
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model
Third-Party Agent Skill: Review the code before installing. Agent skills execute in your AI assistant's environment and can access your files. Learn more about security
Installation for Agentic Skill
View all platforms →skilz install Microck/ordinary-claude-skills/shap skilz install Microck/ordinary-claude-skills/shap --agent opencode skilz install Microck/ordinary-claude-skills/shap --agent codex skilz install Microck/ordinary-claude-skills/shap --agent gemini
First time? Install Skilz: pip install skilz
Works with 14 AI coding assistants
Cursor, Aider, Copilot, Windsurf, Qwen, Kimi, and more...
Extract and copy to ~/.claude/skills/ then restart Claude Desktop
git clone https://github.com/Microck/ordinary-claude-skills cp -r ordinary-claude-skills/skills_all/claude-scientific-skills/scientific-skills/shap ~/.claude/skills/ Need detailed installation help? Check our platform-specific guides:
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Agentic Skill Details
- Repository
- ordinary-claude-skills
- Type
- Non-Technical
- Meta-Domain
- general
- Primary Domain
- general
- Sub-Domain
- learning model skill
- Market Score
- 27.5
Browse Category
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